// Copyright (c) 2020 Huawei Technologies Co., Ltd
// Copyright (c) 2019, Facebook CORPORATION. 
// All rights reserved.
//
// Licensed under the BSD 3-Clause License  (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "ATen/native/npu/utils/OpAdapter.h"

namespace at {
namespace native {
using namespace at::native::npu;

tuple<Tensor&, Tensor&> slogdet_out_npu(
    Tensor& sign,
    Tensor& y,
    const Tensor& self) {
  OpCommand cmd;
  cmd.Name("LogMatrixDeterminant")
      .Input(self)
      .Output(sign)
      .Output(y)
      .Run();

  return std::tie(sign, y);
}

tuple<Tensor, Tensor> slogdet_npu(const Tensor& self) {

  TORCH_CHECK(self.dim() >= 2, "input must be at least 2 dimensions");

  // calculate the output size
  auto outputSize = array_to_small_vector(self.sizes());
  outputSize.erase(outputSize.end() - 2, outputSize.end());

  // construct the output tensor of the NPU
  Tensor sign = OpPreparation::ApplyTensor(self, outputSize);
  Tensor y = OpPreparation::ApplyTensor(self, outputSize);
  
  // calculate the output result of the NPU
  slogdet_out_npu(sign, y, self);

  return std::tie(sign, y);
}

} // namespace native
} // namespace at